Unsupervised machine learning to explore inflammation following cardiopulmonary bypass
Bibliographic record
Abstract
IntroductionCardiac surgery with cardiopulmonary bypass (CPB) often induces systemic inflammatory reaction syndrome (SIRS), affecting postoperative outcome. We aimed to explore adaptive/maladaptive inflammation using unsupervised machine learning.MethodsWe conducted a post hoc analysis of 1908 adult patients who underwent elective cardiac surgery with CPB between June 2016 and June 2020 at a single institution. Patients were assessed for SIRS 12 hours post-surgery and clustered using the partitioning around medoids (PAM) algorithm based on Gower distance. The influence of SIRS on a composite outcome comprising death, stroke/TIA, renal replacement therapy, reoperation for bleeding, mechanical circulatory support, and ICU stay >96 hours was analyzed via multivariable logistic regression.ResultsSIRS occurred in 28.7% of patients (median age 69 years; 68.7% male). Clustering revealed two subgroups: maladaptive SIRS (52.9%) with higher preoperative risk and worse outcomes, and adaptive SIRS (47.1%) with favorable outcomes. Maladaptive SIRS patients had higher 30-day mortality (21.7% vs 1.6%, p < .001). Adaptive SIRS patients had outcomes similar to SIRS-negative controls. In selected clusters, SIRS was independently associated with a lower risk of the composite outcome (OR 0.44; 95% CI 0.26-0.74, p = .002).ConclusionUnsupervised machine learning effectively identifies adaptive and maladaptive SIRS in cardiac surgery patients, providing a basis for personalized postoperative care. Several clinical and procedural factors associated with maladaptive SIRS may be modifiable, supporting future precision strategies to reduce harmful inflammation after cardiac surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".